Cell segmentation

GPU Accelerated Image Processing with CLIJ2

The NEUBIAS Academy at home about CLIJ2 gives an introduction to accelerated image processing using Graphics Processing Units (GPUs) in ImageJ/Fiji. Core concepts are explained as well as usage of the tools with the ImageJ Macro recorder and auto-completion in Fijis script editor. Furthermore, an outlook is provided of how the CLIJ project will develop in the coming years to provide long-term maintained access to GPU-acceleration in the Bio-Image Analysis context.

Image Analysis of Biological Data using CellProfiler

After the session you will be able to built your own CellProfiler pipeline, including:

  • Image data import
  • Object segmentation (e.g. detect nuclei in an image) using the modules "IdentifyPrimaryObjects" and "IdentifySecondaryObjects"
  • Object feature measurements (e.g. measure size, shape and intensity of cells)
  • Measurements export to a spreadsheet
  • Creating and saving quality control images
Description

This workflow applies a Stardist pre-trained model (versatile_fluo or versatile_HE) depending on the input images ie. uses both models for a dataset including both fluorescence (grayscale or RGB where all channels are equal) and H&E stained (RGB where channels are not equal) images.

This version uses tensorflow CPU version (See Dockerfile) to ensure compatibility with a larger number of computers. A GPU version should be possible by adapting the Dockerfile with tensorflow-gpu and/or nvidia-docker images.

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Description

This workflow processes a group of images containing cells with discernible nuclei and segments the nuclei and outputs a binary mask that show where nuclei were detected. It performs 2D nuclei segmentation using pre-trained nuclei segmentation models of Cellpose. And it was developed as a test workflow for Neubias BIAFLOWS Benchmarking tool.

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